Systems and methods for image generation

ABSTRACT

The present disclosure relates to systems and methods for image generation. The methods may include obtaining projection data generated by a scanner; generating, based on a first weighting function, a first image by back-projecting the projection data, the first image having a first region corresponding to a first part of the object; generating, based on a second weighting function, a second image by back-projecting the projection data, the second image having a second region corresponding to the first part of the object, the second region of the second image presenting a better CT number uniformity than the first region of the first image; and generating a third image based on the first image and the second image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This present application is a continuation of U.S. application Ser. No.16/236,595, filed on Dec. 30, 2018, which is a continuation ofInternational Application No. PCT/CN2018/107614, filed on Sep. 26, 2018,designating the United States of America, the contents of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods forgenerating an image, and more specifically, to systems and methods forgenerating a computed tomography (CT) image with reduced artifacts.

BACKGROUND

Generally, a CT system may combine X-ray images taken from variousangles to produce cross-sectional images, i.e., CT images, of an object.The quality of a CT image may be influenced by various factors, such as,the artifacts (e.g., cone beam artifacts) in the CT image, the CT numberuniformity in the CT image, or the like. It is desirable to providesystems and method for generating a CT image with reduced artifacts andimproved CT number uniformity.

SUMMARY

According to a first aspect of the present disclosure, a system isprovided. The system may include at least one storage device thatincludes a set of instructions, and at least one processor incommunication with the at least one storage device. When executing theinstructions, the at least one processor may be configured to: cause thesystem to obtain projection data generated by a scanner; generate, basedon a first weighting function, a first image by back-projecting theprojection data, and the first image may have a first regioncorresponding to a first part of the object; generate, based on a secondweighting function, a second image by back-projecting the projectiondata, the second image may have a second region corresponding to thefirst part of the object, and the second region of the second image maypresent a better CT number uniformity than the first region of the firstimage; and generate a third image based on the first image and thesecond image. The at least one processor may include a parallel hardwarearchitecture having a plurality of processing threads, and the backprojection of the projection data may be performed in parallel withrespect to a voxel in the first image and a corresponding voxel in thesecond image.

In some embodiments, the first image may have fewer artifacts than thesecond image.

In some embodiments, the first image may include better high frequencycomponents than the second image.

In some embodiments, the scanner may further include a radiation sourceconfigured to scan the object along a circular trajectory covering anangle range of 360° to produce the projection data.

In some embodiments, the first part of the object may be radiated by theradiation source at an angle range less than 360°, and the first regionof the first image may include better low frequency components than thesecond region of the second image.

In some embodiments, to generate a third image, the at least oneprocessor may be configured to cause the system to generate a differenceimage of the first image and the second image from each other bysubtraction; and determine the third image based on the difference imageand the first image.

In some embodiments, to determine the third image, the at least oneprocessor may be configured to cause the system to generate a fourthimage by performing a masking operation on the difference image;generate a fifth image by performing a data extrapolation operation onthe fourth image; generate a sixth image by performing a low-passfiltering operation on the fifth image; and combine the sixth image andthe first image to generate the third image.

In some embodiments, the first image may have a plurality of firstvoxels, to generate the first image, the at least one processor may beconfigured to cause the system to: for a first voxel of the plurality offirst voxels, apply, according to the first weighting function, aweighting factor to first projection data corresponding to each of aplurality of projection angles to obtain weighted projection data of thefirst voxel; and back-project the weighted projection data of the firstvoxel to obtain back-projected data of the first voxel; and obtain thefirst image based on the back-projected data of the first voxel.

In some embodiments, the weighting factor applied to the firstprojection data corresponding to a projection angle may be associatedwith a first value of the first weighting function and a second value ofthe first weighting function; the first value of the first weightingfunction may be associated with a first projection point on a detectorwhere radiation from the radiation source at the projection anglestrikes; and the second value of the first weighting function may beassociated with a second projection point on the detector whereradiation from the radiation source at an opposite projection anglestrikes.

In some embodiments, the parallel hardware architecture may include atleast one graphic processing unit, and the at least one graphicprocessing unit may include a plurality of scalar processors.

According to a second aspect of the present disclosure, a method forimage generation is provided. The method may be implemented on at leastone machine each of which includes at least one processor and at leastone storage device. The method may include: obtaining projection datagenerated by a scanner; generating, based on a first weighting function,a first image by back-projecting the projection data, the first imagehaving a first region corresponding to a first part of the object;generating, based on a second weighting function, a second image byback-projecting the projection data, the second image having a secondregion corresponding to the first part of the object, the second regionof the second image presenting a better CT number uniformity than thefirst region of the first image; and generating a third image based onthe first image and the second image, wherein the at least one processorincludes a parallel hardware architecture having a plurality ofprocessing threads, and the back projection of the projection data areperformed in parallel with respect to a voxel in the first image and acorresponding voxel in the second image.

According to a third aspect of the present disclosure, a non-transitorycomputer readable medium embodying a computer program product isprovided. The computer program product may include instructionsconfigured to cause a computing device to: obtain projection datagenerated by a scanner; generate, based on a first weighting function, afirst image by back-projecting the projection data, and the first imagemay have a first region corresponding to a first part of the object;generate, based on a second weighting function, a second image byback-projecting the projection data, the second image may have a secondregion corresponding to the first part of the object, and the secondregion of the second image may present a better CT number uniformitythan the first region of the first image; and generate a third imagebased on the first image and the second image. The at least oneprocessor may include a parallel hardware architecture having aplurality of processing threads, and the back projection of theprojection data may be performed in parallel with respect to a voxel inthe first image and a corresponding voxel in the second image.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating an exemplary image reconstructionmodule according to some embodiments of the present disclosure;

FIG. 6 is a block diagram illustrating an exemplary image generationmodule according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for generatingan image according to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generatingan image according to some embodiments of the present disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for generatingan image according to some embodiments of the present disclosure;

FIG. 10 is a schematic diagram illustrating an exemplary scanning regionaccording to some embodiments of the present disclosure;

FIG. 11 is a schematic diagram illustrating an exemplary coordinatesystem for the scanning of an object according to some embodiments ofthe present disclosure;

FIG. 12 is a schematic diagram illustrating an exemplary apertureweighting function according to some embodiments of the presentdisclosure;

FIG. 13 -A is an exemplary image according to some embodiments of thepresent disclosure;

FIG. 13 -B is an exemplary image generated based on a data extrapolationoperation performed on FIG. 13 -A according to some embodiments of thepresent disclosure;

FIG. 13 -C is an exemplary image generated based on a low-filteringoperation performed on FIG. 13 -B according to some embodiments of thepresent disclosure;

FIGS. 14 -A to 14-C illustrate three exemplary images according to someembodiments of the present disclosure; and

FIGS. 15 -A to 15-C illustrate three exemplary images according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present disclosure and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the presentdisclosure is not limited to the embodiments shown but is to be accordedthe widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particularexemplary embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,” and/or“module” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by another expression if theyachieve the same purpose.

Generally, the word “module” or “unit” as used herein, refers to logicembodied in hardware or firmware, or to a collection of softwareinstructions. A module or a unit described herein may be implemented assoftware and/or hardware and may be stored in any type of non-transitorycomputer-readable medium or another storage device. In some embodiments,a software module/unit may be compiled and linked into an executableprogram. It will be appreciated that software modules can be callablefrom other modules/units or from themselves, and/or may be invoked inresponse to detected events or interrupts. Software modules/unitsconfigured for execution on computing devices (e.g., processor 210 asillustrated in FIG. 2 ) may be provided on a computer-readable medium,such as a compact disc, a digital video disc, a flash drive, a magneticdisc, or any other tangible medium, or as a digital download (and can beoriginally stored in a compressed or installable format that needsinstallation, decompression, or decryption prior to execution). Suchsoftware code may be stored, partially or fully, on a storage device ofthe executing computing device, for execution by the computing device.Software instructions may be embedded in firmware, such as an EPROM. Itwill be further appreciated that hardware modules/units may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units or computing device functionalitydescribed herein may be implemented as software modules/units, but maybe represented in hardware or firmware. In general, the modules/unitsdescribed herein refer to logical modules/units that may be combinedwith other modules/units or divided into sub-modules/sub-units despitetheir physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine or module is referred toas being “on,” “connected to,” or “coupled to,” another unit, engine, ormodule, it may be directly on, connected or coupled to, or communicatewith the other unit, engine, or module, or an intervening unit, engine,or module may be present, unless the context clearly indicatesotherwise. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of the present disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in inverted order, or simultaneously. Moreover, oneor more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

In the present disclosure, an image of an object (e.g., a tissue, anorgan, a tumor, a body, or the like) or a portion thereof (e.g., a partcorresponding to a region of interest in the image) may be referred toas an “image,” a “partial image,” or the object itself. For example, animage of a lung may be referred to as a lung image or lung for brevity,and a region of interest corresponding to the lung image may bedescribed as “the region of interest may include a lung.” In someembodiments, an image may include a two-dimensional (2D) image and/or athree-dimensional (3D) image. The tiniest distinguishable element in animage may be termed as a pixel (in the 2D image) or a voxel (in the 3Dimage). Each pixel or voxel may represent a corresponding point of theobject. For simplicity, the corresponding point of the object may bedescribed as “the pixel” or “the voxel.” For example, the projectiondata of a corresponding point of the object may be described as “theprojection data of the voxel.”

Some embodiments of the present disclosure relate to systems and methodsfor image generation. With the systems and the methods disclosed in thepresent disclosure, at least two original images may be generated basedon same projection data according to different algorithms and processedand/or combined to generate a final image. A first original image may bebetter than a second original image in terms of a first feature, whilethe second original image is better than the first original image interms of a second feature. The first feature or the second feature mayrelate to, e.g., artifact, CT number uniformity, etc. The final imagemay combine the merits of the at least two original images. Thecombination may lead to reduced artifacts and improved CT numberuniformity in the final image. The first feature or the second featuremay be obtained by applying a weighting function to the same projectiondata of the at least two original images. A weighting factor of theweighting function assigned to projection data of a voxel (e.g., a voxelof the first original image or the second original image) correspondingto a projection angle may be a normalized value of a first value of theweighting function and a second value of the weighting function. Thenormalization may for example, provide a good CT number uniformity forthe first original image or the second original image. The systems andthe methods may also achieve a high efficiency by reconstructing the atleast two original images in parallel with a parallel hardwarearchitecture. The parallel hardware architecture may obtain the sameprojection data and then reconstruct the at least two original imagesbased on the same projection data respectively, in parallel, therebyreducing the number of times that the parallel hardware architecturereads the same projection data from, for example, a storage device.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. As shown,the imaging system 100 may include a scanner 110, a processing device120, a network 130, a storage device 140, and one or more terminaldevices 150. In some embodiments, the scanner 110, the processing device120, the storage device 140, and/or the terminal device(s) 150 may beconnected to and/or communicate with each other via a wirelessconnection (e.g., the network 130), a wired connection, or a combinationthereof. The connection between the components in the imaging system 100may be variable. Merely by way of example, the scanner 110 may beconnected to the processing device 120 through the network 130, asillustrated in FIG. 1 . As another example, the scanner 110 may beconnected to the processing device 120 directly. As a further example,the storage device 140 may be connected to the processing device 120through the network 130, as illustrated in FIG. 1 , or connected to theprocessing device 120 directly.

The scanner 110 may generate or provide image data (e.g., projectiondata) via scanning an object, or a part of the object. The scanner 110may include a single-modality scanner and/or a multi-modality scanner.The single-modality scanner may include, for example, a CT scanner, amagnetic resonance imaging (MRI) scanner, a positron emission tomography(PET) scanner, a single photon emission computed tomography (SPECT)scanner, a digital subtraction angiography (DSA) scanner, etc. In someembodiments, the CT scanner may include a cone beam CT (CBCT) scanner.The multi-modality scanner may include a SPECT-CT scanner, a PET-CTscanner, a SPECT-PET scanner, a DSA-MRI scanner, or the like, or anycombination thereof. In some embodiments, the object being scanned mayinclude a portion of a body, a substance, or the like, or anycombination thereof. For example, the object may include a specificportion of a body, such as a head, a thorax, an abdomen, or the like, orany combination thereof. As another example, the object may include aspecific organ, such as an esophagus, a trachea, a bronchus, a stomach,a gallbladder, a small intestine, a colon, a bladder, a ureter, auterus, a fallopian tube, etc.

In some embodiments, the scanner 110 may transmit the image data via thenetwork 130 to the processing device 120, the storage device 140, and/orthe terminal device(s) 150. For example, the image data may be sent tothe processing device 120 for further processing, or may be stored inthe storage device 140.

For illustration purposes, the scanner 110 may be described as a CTscanner. It shall be noted that, in different situations, other types ofscanners as described above may be used to perform the similar functions(e.g., acquiring image data) as the CT scanner. As shown in FIG. 1 , thescanner 110 may include a radiation source 112, a detector 114, and atable 116. The radiation source 112 may scan an object or a portionthereof (e.g., the head, a breast, etc., of a patient) located on thetable 116. The radiation source 112 may be configured to generate and/ordeliver one or more radiation beams to the object. Exemplary radiationbeams may include a particle beam, a photon beam, or the like, or anycombination thereof. A particle beam may include a stream of neutrons,protons, electrons, heavy ions, or the like, or any combination thereof.A photon beam may include an X-ray beam, a γ-ray beam, a β-ray beam, anultraviolet beam, a laser beam, or the like, or any combination thereof.The radiation beam may have the shape of a line, a narrow pencil, anarrow fan, a fan, a cone, a wedge, a tetrahedron, or the like, or anycombination thereof. In some embodiments, the radiation source 112 maybe a CBCT radiation source and the radiation beam may be a cone beam.

The detector 114 may detect one or more radiation beams emitted from theradiation source 112 or scattered by the object to generate image data(e.g., projection data). The image data may be transmitted to theprocessing device 120 for further processing. For example, theprocessing device 120 may reconstruct an image of the object or aportion thereof based on the image data.

In some embodiments, the detector 114 may include one or more detectorunits. A detector unit may include a scintillator detector (e.g., acesium iodide detector, a gadolinium oxysulfide detector), a gasdetector, etc. In some embodiments, the detector units may be arrangedin a single row, two rows, or any other number of rows. Merely by way ofexample, the detector 114 may be a CT detector configured to detectX-rays.

The processing device 120 may process data and/or information obtainedfrom the scanner 110, the storage device 140, and/or the terminaldevice(s) 150. For example, the processing device 120 may reconstructone or more images based on the projection data collected by the scanner110. In some embodiments, the processing device 120 may reconstruct morethan one (e.g., two, three) images based on a same set of projectiondata that is acquired by the scanner 110 by scanning a same object. Insome embodiments, the more than one images associated with the same setof projection data may be reconstructed by a processor having a parallelhardware architecture. The hardware architecture may perform operations(e.g., calculating the back-projection (BP) values of voxels indifferent images) in a parallel manner. In some embodiments, theprocessing device 120 may further process the reconstructed images by,for example, image filtering, eliminating saltation or noises in animage, image combination, or the like, or any combination thereof.

In some embodiments, the processing device 120 may be a single server,or a server group. The server group may be centralized, or distributed.In some embodiments, the processing device 120 may be local or remote.For example, the processing device 120 may access information and/ordata stored in the scanner 110, the storage device 140, and/or theterminal device(s) 150 via the network 130. As another example, theprocessing device 120 may be directly connected to the scanner 110, thestorage device 140, and/or the terminal device(s) 150 to access storedinformation and/or data. As a further example, the processing device 120may be integrated in the scanner 110. In some embodiments, theprocessing device 120 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof. Insome embodiments, the processing device 120 may be implemented in acomputing device 200 having one or more components illustrated in FIG. 2in the present disclosure.

The network 130 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., thescanner 110, the processing device 120, the storage device 140, and/orthe terminal device 150(s)) may communicate information and/or data withone or more other components of the imaging system 100 via the network130. For example, the processing device 120 may obtain image data fromthe scanner 110 via the network 130. As another example, the processingdevice 120 may obtain user instructions from the terminal device(s) 150via the network 130. The network 130 may include a public network (e.g.,the Internet), a private network (e.g., a local area network (LAN), awide area network (WAN)), etc.), a wired network (e.g., an Ethernetnetwork), a wireless network (e.g., an 802.11 network, a Wi-Fi network,etc.), a cellular network (e.g., a Long Term Evolution (LTE) network), aframe relay network, a virtual private network (“VPN”), a satellitenetwork, a telephone network, routers, hubs, witches, server computers,or the like, or any combination thereof. For example, the network 130may include a cable network, a wireline network, a fiber-optic network,a telecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 130 may include one or more network accesspoints. For example, the network 130 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the imaging system 100may be connected to the network 130 to exchange data and/or information.

The storage device 140 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 140 may store dataobtained from the scanner 110, the processing device 120 and/or theterminal device(s) 150. In some embodiments, the storage device 140 maystore data and/or instructions that the processing device 120 mayexecute or use to perform exemplary methods described in the presentdisclosure. For example, the storage device 140 may store projectiondata obtained from the scanner 110. The processing device 120 mayfurther access the projection data and reconstruct one or more imagesbased on the projection data.

In some embodiments, the storage device 140 may include mass storage,removable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), a digital versatile disk ROM,etc. In some embodiments, the storage device 140 may be implemented on acloud platform as described elsewhere in the disclosure.

In some embodiments, the storage device 140 may be connected to thenetwork 130 to communicate with one or more other components in theimaging system 100 (e.g., the scanner 110, the processing device 120 orthe terminal device(s) 150). One or more components in the imagingsystem 100 may access the data or instructions stored in the storagedevice 140 via the network 130. In some embodiments, the storage device140 may be directly connected to or communicate with one or morecomponents of the imaging system 100 (e.g., the processing device 120,the terminal device(s) 150). In some embodiments, the storage device 140may be part of the processing device 120.

The terminal device(s) 150 may be connected to and/or communicate withthe scanner 110, the processing device 120, the network 130, and/or thestorage device 140. In some embodiments, the scanner 110 may be operatedfrom the terminal device(s) 150 via, e.g., a wireless connection. Insome embodiments, the terminal device(s) 150 may receive informationand/or instructions inputted by a user, and send the receivedinformation and/or instructions to the scanner 110 or to the processingdevice 120 via the network 130. In some embodiments, the terminaldevice(s) 150 may receive data and/or information from the processingdevice 120 and/or the scanner 110. For example, the terminal device(s)150 may receive a processed image from the processing device 120. Asanother example, the terminal device(s) 150 may obtain image dataacquired via the scanner 110 and transmit the image data to theprocessing device 120. In some embodiments, the terminal device(s) 150may be part of or communicate with the processing device 120. In someembodiments, the terminal device(s) 150 may be omitted.

In some embodiments, the terminal device(s) 150 may include a mobiledevice 151, a tablet computer 152, a laptop computer 153, or the like,or any combination thereof. The mobile device 151 may include a smarthome device, a wearable device, a smart mobile device, a virtual realitydevice, an augmented reality device, or the like, or any combinationthereof. In some embodiments, the smart home device may include a smartlighting device, a control device of an intelligent electricalapparatus, a smart monitoring device, a smart television, a smart videocamera, an interphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a smart bracelet, smartfootgear, a pair of smart glasses, a smart helmet, a smart watch, smartclothing, a smart backpack, a smart accessory, or the like, or anycombination thereof. In some embodiments, the smart mobile device mayinclude a smartphone, a personal digital assistant (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, or the like,or any combination thereof. In some embodiments, the virtual realitydevice and/or the augmented reality device may include a virtual realityhelmet, a virtual reality glass, a virtual reality patch, an augmentedreality helmet, an augmented reality glass, an augmented reality patch,or the like, or any combination thereof. For example, the virtualreality device and/or the augmented reality device may include a GoogleGlass™, an Oculus Rift™, a Hololens™, a Gear VR™, etc.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. For example, the storagedevice 140 may be a data storage including cloud computing platforms,such as, public clouds, private clouds, community clouds, hybrid clouds,etc. In some embodiments, the processing device 120 may be integratedinto the scanner 110. However, those variations and modifications do notdepart the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device 200 on which theprocessing device 120 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2 , the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (program code) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. In some embodiments, the processor 210 mayprocess data obtained from the scanner 110, the storage device 140, theterminal device(s) 150, and/or any other component of the imaging system100. For example, the processor 210 may reconstruct one or more imagesbased on projection data obtained from the scanner 110. In someembodiments, the reconstructed image may be stored in the storage device140, the storage 220, etc. In some embodiments, the reconstructed imagemay be displayed on a display device by the I/O 230. In someembodiments, the processor 210 may perform instructions obtained fromthe terminal device(s) 150. In some embodiments, the processor 210 mayinclude one or more hardware processors, such as a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combination thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both process A and process B, it should be understood thatprocess A and process B may also be performed by two or more differentprocessors jointly or separately in the computing device 200 (e.g., afirst processor executes process A and a second processor executesprocess B, or the first and second processors jointly execute processesA and B).

The storage 220 may store data/information obtained from the scanner110, the storage device 140, the terminal device(s) 150, or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program or algorithm, when executedby the processing device 120, may reduce artifacts in an image. In someembodiments, the storage 220 may store one or more intermediate resultsgenerated during an image reconstruction process. For example, thestorage 220 may store one or more BP values calculated according to theprojection data. The stored BP values may be further retrieved by theprocessor 210 or any other processing component in the image system 110for further processing (e.g., image reconstruction).

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or anycombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or anycombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or any combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 130) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and thescanner 110, the storage device 140, or the terminal device(s) 150. Theconnection may be a wired connection, a wireless connection, orcombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include Bluetooth™, Wi-Fi, WiMax, WLAN, ZigBee™, mobilenetwork (e.g., 3G, 4G, 5G, etc.), or the like, or any combinationthereof. In some embodiments, the communication port 240 may be astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 according to someembodiments of the present disclosure. In some embodiments, theprocessing device 120 and/or the terminal device(s) 150 may beimplemented on the mobile device 300 via its hardware, software program,firmware, or any combination thereof. As illustrated in FIG. 3 , themobile device 300 may include a communication platform 310, a display320, a graphic processing unit (GPU) 330, a central processing unit(CPU) 340, an I/O 350, a memory 360, and a storage 390. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 300. In some embodiments, a mobile operating system 370(e.g., iOS™, Android™, Windows Phone™, etc.) and one or moreapplications 380 may be loaded into the memory 360 from the storage 390in order to be executed by the CPU 340. The applications 380 may includea browser or any other suitable mobile apps for receiving and renderinginformation relating to image processing or other information from theprocessing device 120. User interactions with the information stream maybe achieved via the I/O 350 and provided to the processing device 120and/or other components of the imaging system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or other type of work station or terminaldevice, although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result the drawings should be self-explanatory.

FIG. 4 is block diagram illustrating an exemplary processing device 120according to some embodiments of the present disclosure. The processingdevice 120 may include an obtaining module 410, an image reconstructionmodule 420, and an image generation module 430. At least a portion ofthe processing device 120 may be implemented on a computing device asillustrated in FIG. 2 or a mobile device as illustrated in FIG. 3 .

The obtaining module 410 may obtain projection data. In someembodiments, the obtaining module 410 may obtain the projection datafrom the scanner 110, the storage device 140, the terminal device(s)150, and/or an external data source (not shown). In some embodiments,the projection data may be generated based on detected radiation beams(e.g., X-ray beams) at least some of which have passed through an objectbeing radiated in the scanner 110. The object may include substance,tissue, an organ, a specimen, a body, or the like, or any combinationthereof. In some embodiments, the object may include a head, a breast, alung, a pleura, a mediastinum, an abdomen, a long intestine, a smallintestine, a bladder, a gallbladder, a triple warmer, a pelvic cavity, abackbone, extremities, a skeleton, a blood vessel, or the like, or anycombination thereof. In some embodiments, the projection data may betransmitted to the image reconstruction module 420 for furtherprocessing. The image reconstruction module 420 may reconstruct at leastone image of the object or a portion thereof based on the projectiondata. In some embodiments, the projection data may be transmitted to astorage module of the processing device 120 to be stored.

The image reconstruction module 420 may reconstruct one or more imagesbased on projection data (e.g., the projection data obtained from theobtaining module 410, the storage module of the processing device 120,and/or the storage device 140). In some embodiments, the imagereconstruction module 420 may reconstruct an image according to areconstruction technique including, for example, an iterativereconstruction algorithm (e.g., a statistical reconstruction algorithm),a Fourier slice theorem algorithm, a filtered back projection (FBP)algorithm, a fan-beam reconstruction algorithm, an analyticreconstruction algorithm, or the like, or any combination thereof. Insome embodiments, the image reconstruction module 420 may reconstructmore than one (e.g., two, three) images based on a same set ofprojection data of an object according to a weighting function. Theimage reconstruction module 420 may apply the weighting function to thesame set of projection data of the object to obtain weighted projectiondata, and back-project the weighted projection data to generate an imageof the object. In some embodiments, weighting functions associated withthe more than one images may be different. In some embodiments, the morethan one images associated with the same set of projection data may bereconstructed by a processor having a parallel hardware architecture.The parallel hardware architecture may perform operations (e.g.,calculating the BP values of voxels in different images) in a parallelmanner. More descriptions of the weighting function and/or thereconstruction of an image may be found elsewhere in the disclosure(e.g., FIG. 7 and/or FIG. 9 and the descriptions thereof). In someembodiments, the image reconstruction module 420 may performpre-processing operations on the projection data before thereconstruction. Exemplary pre-processing operation may include,projection data normalization, projection data smoothing, projectiondata suppressing, projection data encoding (or decoding), preliminarydenoising, etc.

The image generation module 430 may generate a final image based on atleast two original images. In some embodiments, the image generationmodule 430 may obtain the at least one two original images from theimage reconstruction module 420, a storage module of the processingdevice 120, the storage device 140, or the terminal device(s) 150. Theimage generation module 430 may generate the final image based on one ormore operations including, for example, an image subtraction operation,a masking operation, a data extraction operation, a low-pass filteringoperation, an image combination operation, etc. A first original imagemay be better than a second original image in terms of a first feature(e.g., a feature relating to artifact), while the second original imageis better than the first original image in terms of a second feature(e.g., a feature relating to CT number uniformity). The final image maycombine the merits of the at least two original images. For example, thefinal image may present reduced artifacts and improved CT numberuniformity. More descriptions of the generation of the final image maybe found elsewhere in the disclosure (e.g., FIG. 8 and the descriptionthereof).

In some embodiments, one or more modules illustrated in FIG. 5 may beimplemented in at least part of the imaging system 100 as illustrated inFIG. 1 . For example, the obtaining module 410, the image reconstructionmodule 420, and/or the image generation module 430 may be integratedinto a console (not shown). Via the console, a user may set parametersfor scanning an object, controlling the imaging processes, adjustingparameters for reconstructing an image, etc. In some embodiments, theconsole may be implemented via the processing device 120 and/or theterminal device(s) 150.

FIG. 5 is a block diagram illustrating an exemplary image reconstructionmodule 420 according to some embodiments of the present disclosure. Theimage reconstruction module 420 may include a weighting unit 510 and aback projection unit 520. At least a portion of the image reconstructionmodule 420 may be implemented on a computing device as illustrated inFIG. 2 or a mobile device as illustrated in FIG. 3 .

The weighting unit 510 may perform a weighting operation on projectiondata. For example, the weighting unit 510 may assign a weighting factorto projection data of a voxel to obtain weighted projection data of thevoxel. In some embodiments, the weighting factor may be determinedaccording to a weighting function. More descriptions of the weightingoperation may be found elsewhere in the disclosure (e.g., FIG. 9 and thedescription thereof). In some embodiments, the weighted projection datamay be transmitted to the back projection unit 520 for back-projectingthe weighted projection data.

The back projection unit 520 may back-project projection data of avoxel. For example, the back projection unit 520 may back-project theweighted projection data of the voxel obtained from the weighting unit510 to obtain back-projected data of the voxel. In some embodiments, theback projection unit 520 may perform the back-projection operation bytransforming the projection data from a projection domain to an imagedomain.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theweighting unit 510 and the back projection unit 520 may be integratedinto an independent unit. As another example, the image reconstructionmodule 420 may include a storage unit.

FIG. 6 is a block diagram illustrating an exemplary image generationmodule 430 according to some embodiments of the present disclosure. Theimage generation module 430 may include an image subtraction unit 610,an image masking unit 620, a data extrapolation unit 630, a low-passfiltering unit 640, and an image combination unit 650. At least aportion of the image generation module 430 may be implemented on acomputing device as illustrated in FIG. 2 or a mobile device asillustrated in FIG. 3 .

The image subtraction unit 610 may generate a difference image based ona first image and a second image by, e.g., subtraction. In someembodiments, the image subtraction unit 610 may obtain the first imageand/or the second image from the image reconstruction module 420, thestorage device 140, a storage module of the processing device 120, or anexternal storage device. In some embodiments, the image subtraction unit610 may generate the difference image by subtracting one image from theother. For example, the image subtraction unit 610 may generate thedifference image by subtracting the first image from the second image.As another example, the image subtraction unit 610 may generate thedifference image by subtracting the second image from the first image.More descriptions of the subtraction may be found elsewhere in thedisclosure (e.g., FIG. 8 and the description thereof). In someembodiments, the difference image may be transmitted to the imagemasking unit 620 for performing a masking operation on the differenceimage. In some embodiments, the first image may be transmitted to theimage combination unit 650 for combining the first image with anotherimage.

The image masking unit 620 may perform a masking operation on an image.For example, the image masking unit 620 may generate a fourth image byperforming a masking operation on the difference image obtained from theimage subtraction unit 610. In some embodiments, the masking operationmay include applying a mask (e.g., a two-dimensional matrix, athree-dimensional matrix) on the difference image. By applying the maskon the difference image (e.g., making the mask multiply by thedifference image), the image masking unit 620 may reset the values ofthe pixels/voxels (e.g., gray values) in a region of the differenceimage to a default value (e.g., “0” for gray value), and the values ofthe pixels/voxels (e.g., gray values) in another region of thedifference image may remain unchanged. More descriptions of the makingoperation may be found elsewhere in the disclosure (e.g., FIG. 8 and thedescription thereof). In some embodiments, the fourth image may betransmitted to the data extrapolation unit 630 for performing a dataextrapolation operation on the fourth image.

The data extrapolation unit 630 may perform a data extrapolationoperation on an image. For example, the data extrapolation unit 630 maygenerate a fifth image by performing a data extrapolation operation onthe fourth image obtained from the image masking unit 620. A dataextrapolation operation may be a process in which the value of aspecific data point is estimated based on the values of data points inthe vicinity of the specific data point in space. By performing the dataextrapolation operation on the fourth image, the data extrapolation unit630 may assign the values of a pixel/voxel (e.g., gray value) in aregion of the fourth image based on the values of pixels/voxels (e.g.,gray values) in another region of the fourth image. More descriptions ofthe data extrapolation operation may be found elsewhere in thedisclosure (e.g., FIG. 8 and the description thereof). In someembodiments, the fifth image may be transmitted to the low-passfiltering unit 640 for performing a low-filtering operation on the fifthimage.

The low-pass filtering unit 640 may perform a low-pass filteringoperation on an image. For example, the low-pass filtering unit 640 maygenerate a sixth image by performing a low-pass filtering operation onthe fifth image obtained from the data extrapolation unit 630. Thelow-pass filtering unit 640 may perform the low-pass filtering operationon the fifth image using a low-pass filter, such as, a Gaussian low-passfilter, a Butterworth low-pass filter, a Chebyshev low-pass filter, a 3Dbox filter (e.g., a 3×3×3 box filter), or the like, or any combinationthereof. More descriptions of the low-pass filtering operation may befound elsewhere in the disclosure (e.g., FIG. 8 and the descriptionthereof). In some embodiments, the sixth image may be transmitted to theimage combination unit 650 for combining the sixth image with anotherimage.

The image combination unit 650 may combine at least two images. Forexample, the image combination unit 650 may combine the sixth imageobtained from the low-pass filtering unit 640 and the first imageobtained from the image reconstruction module 420. The image combinationmay refer to a combination of data (e.g., gray value) of correspondingpixels/voxels in the sixth image and the first image. During the imagecombination, the image generation module 430 may apply one or more ofvarious operations including, for example, an addition operation, asubtraction operation, a multiplication operation, a division operation,or any combination thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theimage generation module 430 may include a storage unit for storingimages obtained from the image subtraction unit 610, the image maskingunit 620, the data extrapolation unit 630, the low-pass filtering unit640, and/or the image combination unit 650.

FIG. 7 is a flowchart illustrating an exemplary process 700 forgenerating an image according to some embodiments of the presentdisclosure. The process 700 may be executed by the imaging system 100.For example, the process 700 may be stored in the storage device 140and/or the storage 220 in the form of instructions (e.g., anapplication), and invoked and/or executed by the processing device 120(e.g., the processor 210 illustrated in FIG. 2 , or one or more modulesin the processing device 120 illustrated in FIG. 4 ). The operations ofthe illustrated process presented below are intended to be illustrative.In some embodiments, the process 700 may be accomplished with one ormore additional operations not described, and/or without one or more ofthe operations discussed. Additionally, the order in which theoperations of the process 700 as illustrated in FIG. 7 and describedbelow is not intended to be limiting.

In 710, the processing device 120 (e.g., the obtaining module 410) mayobtain projection data generated by a scanner. For example, theprocessing device 120 may obtain the projection data from the scanner110 via, for example, the network 130. The scanner may acquire theprojection data by scanning an object (e.g., a head, a breast). Forexample, a radiation source of the scanner (e.g., the radiation source112 of the scanner 110) may emit radiation beams (e.g., X-ray beams) tothe object. A detector of the scanner (e.g., the detector 114 of thescanner 110) may acquire the projection data based on detected radiationbeams, at least some of which may have passed through the object.

In some embodiments, the radiation source may scan the object byemitting radiation beams toward the object from different projectionangles to produce the projection data. The different projection anglesmay fall within an angle range. For example, the radiation source mayscan the object along a circular trajectory covering an angle range of360°. As another example, the radiation source may scan the object alonga trajectory covering an angle range less than 360° (e.g., 90°, 180°,240°). As used herein, a projection angle is the angle between the lineconnecting the radiation source and the origin of a coordinate systemand an axis of the coordinate system, e.g., the positive X axis of thecoordinate system. See, for example, FIG. 11 providing a schematicdiagram of an exemplary coordinate system for the scanning of an object.As shown in FIG. 11 , the radiation source of a scanner may scan anobject 1102 along a circular trajectory 1103. The scanner may be a CBCTscanner and the radiation source may emit cone beams to the object 1102.The isocenter O of the scanner may coincide with the origin of thecoordinate system that is an XYZ coordinate system as illustrated. Whenthe radiation source is located at the position 1101, it may correspondto a projection angle β, which is the angle between the line connectingthe radiation source and the origin O (i.e., line O O′) and the positiveX axis of the XYZ coordinate system.

In 720, the processing device 120 (e.g., the image reconstruction module420) may generate, based on a first weighting function, a first image byback-projecting the projection data. In some embodiments, the firstimage may be a 2D image, a 3D image, a four-dimensional (4D) image, etc.

The first image may have different regions corresponding to differentparts of the object. For example, the first image may have a specificregion corresponding to a specific part of the object. In someembodiments, due to the shape of the radiation beams emitted from theradiation source, the specific part of the object may not be radiated bythe radiation source from a specific projection angle.

For example, FIG. 10 illustrates an exemplary scanning region accordingto some embodiments of the present disclosure. As illustrated in FIG. 10, a scanning region 1002 may show a cross section of an object that isscanned by a radiation source of a scanner moving along a circulartrajectory. The rotation center of the circular trajectory may be theisocenter of the scanner, which is denoted by O in FIG. 10 . Thecircular trajectory passes through positions 1001 and 1008, and islocated in a plane 1003 perpendicular to the scanning region 1002. Thescanner may be a CBCT scanner whose radiation source emits cone beams. Rmay denote the distance between the radiation source at the position1001 and the isocenter O.

The scanning region 1002 may include four corner regions 1004-1, 1004-2,1004-3, and 1004-4, and a center region 1006. Each of the four cornerregions 1004-1, 1004-2, 1004-3, and 1004-4 is represented by atriangular shape in a plane (e.g., a cross section of the object) of thescanning region 1002 and may show a first part of the object located ina corner region of the image or scanning region 1002. The center region1006 may show a second part of the object located in a center region ofthe scanning region 1002. For brevity, a part of the object located in acorner region of the scanning region 1002 in a scan may be referred toas a corner part of the object, and a part of the object located in acenter region of the scanning region 1002 in a scan may be referred toas a center part of the object. For brevity, a corner region of thescanning region may correspond to a corner region of an image generatedbased on projection data acquired by scanning an object located in thescanning region. Similarly, a center region of the scanning region maycorrespond to a center region of an image generated based on projectiondata acquired by scanning an object located in the scanning region. Forbrevity, the object is assumed to be positioned in the scanning regionsuch that a center part of the object is located in the center region ofthe scanning region and accordingly a center region of a generated imageand that a corner part of the object is located in a corner region ofthe scanning region and accordingly a corner region of a generatedimage. When the radiation source is located at the position 1001, thelines 1007 and 1009 may delineate the boundary of the radiation beamsthat can be detected by the detector. The lines 1007 and 1009 may form acone angle 1005 in the plane of the image 1002. The corner regions1004-1 and 1004-3 are not radiated by the radiation source from theposition 1001; in contrast, the corner regions 1004-2 and 1004-4 and thecenter region 1006 are radiated by the radiation source from theposition 1001. Similarly, when the radiation source is located at theposition 1008, the corner regions 1004-2 and 1004-4 are not radiated bythe radiation source, and the corner regions 1004-1, 1004-3 and thecenter region 1006 are radiated by the detector. Therefore, the cornerparts of the object may be radiated by the radiation source from anglesin an angle range less than 360°, and the center part of the object maybe radiated by the radiation source from angles in an angle range of360°.

It shall be appreciated that the lack of radiation at specificprojection angles may lead to insufficient projection data for a certainpart of the object (e.g., a corner part), and cause one or more ofvarious deficiencies, such as, artifacts, reduction of CT numberuniformity, etc., in a reconstructed image of the object. To cure theseand other deficiencies, the processing device 120 may apply a firstweighting function to the projection data of the object to obtain firstweighted projection data, and back-project the first weighted projectiondata to generate the first image of the object. When the processingdevice 120 applies the first weighting function to the projection dataof the object, the projection data corresponding to a center part of theobject (e.g., the projection data generated from radiation beams onlypassing through the center part of the object) may be assigned aweighting factor different from the projection data corresponding to acorner part of the object (e.g., the projection data generated fromradiation beams passing through the corner part of the object). Forexample, as illustrated in FIG. 10 , the projection data generated fromthe radiation beams that pass through the corner regions 1004-2 and1004-4 may be assigned a lower weighting factor than that of theprojection data generated from the radiation beams that only passthrough the center region 1006 according to the first weightingfunction. In some embodiments, the first weighting function may bedescribed as an aperture weighting function. The weighting factorapplied to the projection data may be associated with the projectionangle corresponding to the projection data. More descriptions of anaperture weighting function may be found elsewhere in the disclosure.See, e.g., FIG. 12 and the description thereof.

In 730, the processing device 120 (e.g., the image reconstruction module420) may generate, based on a second weighting function, a second imageby back-projecting the projection data. In some embodiments, the secondimage may be a 2D image, a 3D image, a 4D image, etc.

The second image may show the same part of the object as the firstimage. For example, the second image may also have a corner region(e.g., the corner region 1004-1, 1004-2, 1004-3, or 1004-4 illustratedin FIG. 10 ) corresponding to one of the corner parts of the object. Thesecond image may also have a center region (e.g., the center region 1006illustrated in FIG. 10 ) corresponding to the center part of the object.In some embodiments, the size of the first image may be same as the sizeof the second image. Alternatively or additionally, the voxel (or pixel)count of the first image may be same as that of the second image. Insome embodiments, the size of the first image may be different from thesize of the second image. At least a portion of the first image and atleast a portion of the second image may correspond to a same portion ofthe object.

Similar to the generation of the first image as illustrated in 720, theprocessing device 120 may apply the second weighting function to theprojection data to obtain second weighted projection data, andback-project the second weighted projection data to generate the secondimage of the object. When the processing device 120 applies the secondweighting function to the projection data of the object, the projectiondata corresponding to a center part of the object (e.g., the projectiondata generated from radiation beams only passing through the center partof the object) may be assigned a weighting factor different from theprojection data corresponding to a corner part of the object (e.g., theprojection data generated from radiation beams passing through thecorner part of the object).

The first weighting function and the second weighting function mayaffect the qualities (e.g., artifact, CT number uniformity) of the firstimage and the second image. In some embodiments, the first weightingfunction and the second weighting function may be derived from a same ordifferent aperture weighting functions. For example, one or moreparameters in a same aperture weighting function, when assigneddifferent values, may produce the first weighting function and thesecond weighting function, respectively. Merely by way of example, thesame aperture weighting function may include a first parameter and asecond parameter. The values of the first parameter and the secondparameter may affect the qualities of the first image and the secondimage. In some embodiments, the first weighting function and the secondweighting function may be assigned the same value of the secondparameter. Additionally, the first weighting function may be assigned asmaller value of the first parameter than the second weighting function.Accordingly, as described elsewhere in the disclosure, the first imagemay include fewer artifacts and better high frequency components thanthe second image. Further, a corner region of the first image mayinclude better low frequency components than the corresponding cornerregion of the second image. Moreover, the second image may present abetter CT number uniformity than the first image. The center region ofthe second image may include better low frequency components than thecorresponding center region of the first image.

In some embodiments, the processing device 120 may obtain the firstweighting function and/or the second weighting function from the storagedevice 140. The first weighting function and the second weightingfunction may be configured to emphasize and/or suppress differentfeatures of the projection data corresponding to different regions ofthe scanning region (and different parts of the object located in thesedifferent regions of the scanning region). For example, a plurality ofweighting functions, including one or more aperture weighting functions,may be stored in the storage device 140 in the form of a lookup table,and thus the processing device 120 may retrieve the first weightingfunction and/or the second weighting function from the lookup table.More descriptions of an aperture weighting function may be foundelsewhere in the disclosure. See, e.g., FIG. 12 and the descriptionthereof.

In some embodiments, the processing device 120 may include a parallelhardware architecture having a plurality of processing threads that canexecute two or more operations concurrently. For example, a processingthread “A” and a processing thread “B” may perform at least part of theoperation 720 and at least part of the operation 730 concurrently. Forillustration purposes, after the acquisition of the projection datacorresponding to a voxel at a specific projection angle, the processingthread “A” and the processing thread “B” may determine the values of thevoxel in the first and second images, respectively, in parallel.Specifically, the processing thread “A” may determine the weightingfactor to be applied on the voxel according to the first weightingfunction, and concurrently, the processing thread “B” may determine theweighting factor to be applied on the voxel according to the secondweighting function. Alternatively or additionally, the processing thread“A” may perform the weighting operation on the projection data withrespect to the voxel in the first image according to the first weightingfunction, and concurrently, the processing thread “B” may perform theweighting operation on the projection data with respect to the voxel inthe second image according to the second weighting function.Alternatively or additionally, the processing thread “A” may perform theback-projection operation on the weighted projection data with respectto the voxel in the first image, and concurrently, the processing thread“B” may perform the back-projection operation on the weighted projectiondata with respect to the voxel in the second image. In some embodiments,the parallel hardware architecture may include one or more graphicprocessing units. A graphic processing unit may include a plurality ofscalar processors.

In some embodiments, the first image and the second image may be CTimages. The first image and the second image may present differentqualities with respect to CT number uniformity. For a CT image, CTnumbers may indicate the attenuation distribution of the radiation beamsthat traverse the object. The CT number uniformity of a CT image mayrefer to the consistency of CT numbers of a homogeneous material (e.g.,water, bone) in the image. In some embodiments, the CT number may berepresented by the Hounsfield unit (HU). Merely by way of example, theCT number of water may be 0 HU, and the CT number of air may be −1000HU.

In some embodiments, the second image may present a better CT numberuniformity than the first image. The CT number uniformity of an image(e.g., the first image, the second image) may be expressed as anabsolute value of the difference between the mean value of CT numbers ofa homogeneous material (e.g., water, bone) in the center region of theimage and the mean value of CT numbers of the homogeneous material in acorner region of the image. For example, the CT number uniformity of thesecond image may be within ±5 HU (the absolute value is 5 HU), and theCT number uniformity of the first image may be within ±7 HU (theabsolute value is 7 HU). Therefore, the second image may present abetter CT number uniformity than the first image.

In some embodiments, a corner region of the second image may present abetter CT number uniformity than the corresponding corner region of thefirst image. For example, the CT number uniformity of the corner regionof the second image may be within ±5 HU (the absolute value is 5 HU),and the CT number uniformity of the corresponding corner region of thefirst image may be within ±7 HU (the absolute value is 7 HU). Therefore,the corner region of the second image may present a better CT numberuniformity than the corresponding corner region of the first image.

Alternatively, in some embodiments, the CT number uniformity of thefirst image (and/or the second image) or a region thereof may bedescribed according to Equation (1):

$\begin{matrix}{{u = {\left( {1 - \frac{{CT_{\max}} - {CT_{\min}}}{{CT_{\max}} + {CT_{\min}}}} \right) \times 100\%}},} & {{Equation}(1)}\end{matrix}$where u represents the CT number uniformity of the first image or aregion thereof, CT_(max) represents the maximum CT number of ahomogeneous material (e.g., water, bone) in the first image or a regionthereof, and CT_(min) represents the minimum CT number of thehomogeneous material in the first image or a region thereof. Forexample, according to Equation (1), the CT number uniformity of a region(e.g., the center region) of the second image may be 90%, and the CTnumber uniformity of the corresponding region (e.g., the center region)of the first image may be 50%. Therefore, the region of the second imagemay present a better CT number uniformity than the corresponding regionof the first image. As another example, according to Equation (1), theCT number uniformity of the second image may be 90%, and the CT numberuniformity of the first image may be 50%. Therefore, the second imagemay present a better CT number uniformity than the first image.

In some embodiments, the difference of the CT number uniformity betweenthe first image and the second image may be reflected in the brightnessof the first image and the second image. For example, compared to thesecond image, the overall brightness of the first image may berelatively inconsistent, in that the average brightness of a part (e.g.,a part on the left side) of the first image may be significantly lowerthan another part (e.g., a part on the right side) of the first image.The image illustrated in FIG. 15 -A may be an exemplary first image, theimage in FIG. 15 -C may be an exemplary second image, and the image inFIG. 15 -B may be an exemplary combined image that will be described inconnection with the process 800 in FIG. 8 . As shown in FIG. 15 -A, theaverage brightness of the left region indicated by the arrow E issignificantly lower than that of the right region indicated by the arrowF. As shown in FIG. 15 -C, the average brightness of the left regionindicated by the arrow E′ is relatively close to that of the rightregion indicated by the arrow F′. Therefore, the second image in FIG. 15-C presents a better CT number uniformity than the first image in FIG.15 -A.

In some embodiments, the first image may have fewer artifacts than thesecond image. An artifact may be a distortion or error in an image thatis irrelevant to the object being imaged. The artifacts in the firstimage and/or the second image may include, for example, streakartifacts, ring artifacts, motion artifacts, metal artifacts, or thelike, or any combination thereof. As illustrated in FIG. 15 -C,artifacts (e.g., streaks or dark bands) are present in the regionsindicated by the arrows A′, B′, and C′. In contrast, the image in FIG.15 -A includes significantly reduced artifacts in corresponding regionsindicated by the arrows A, B, and C. Similarly, the image in FIG. 14 -Amay be an exemplary first image, and the image in FIG. 14 -C may be anexemplary second image. As illustrated in FIG. 14 -C, artifacts arepresent in the regions indicated by the arrows A′ and B′. In contrast,the image in FIG. 14 -A includes significantly reduced artifacts incorresponding regions indicated by the arrows A and B.

In some embodiments, the first image may include better high frequencycomponents than the second image. Additionally or alternatively, acorner region of the first image may include better low frequencycomponents than the corresponding corner region of the second image. Asused herein, a region of an image may be referred to as corresponding toa region of another image when each of the regions of the two imagescorresponds to a same part of an object represented in the images. Acenter region of the second image may include better low frequencycomponents than the corresponding center region of the first image. Ahigh frequency component of an image may be a component where thevoxel/pixel values (e.g., gray values) change rapidly from one value toanother. For example, a sharp edge present in an image may include morehigh frequency components than a region having a solid color. A lowfrequency component of an image may be a component where the voxel/pixelvalues (e.g., gray values) change slowly and gradually. For example, aregion having a solid color may include more low frequency componentsthan a sharp edge presented in the image.

The quality of the high frequency components and/or the low frequencycomponents in a specific region of an image may be reflected inartifacts in the specific region. For example, the region indicated bythe arrow A in FIG. 15 -A (e.g., the first image) includes fewerartifacts than the region indicated by the arrow A′ in FIG. 15 -C (e.g.,the second image), and thus the high frequency components in the regionindicated by the arrow A may be considered better than the highfrequency components in the region indicated by the arrow A′. As anotherexample, the region indicated by the arrow C in FIG. 15 -A (e.g., thefirst image) includes fewer artifacts than the region indicated by thearrow C′ in FIG. 15 -C (e.g., the second image), and thus the lowfrequency components in the region indicated by the arrow C may beconsidered better than the low frequency components in the regionindicated by the arrow C′.

In 740, the processing device 120 (e.g., the image generation module430) may generate a third image based on the first image and the secondimage. In some embodiments, the processing device 120 may generate thethird image based on one or more operations including, for example, animage subtraction operation, a masking operation, a data extractionoperation, a low-pass filtering operation, an image combinationoperation, etc. In some embodiments, the operation 740 may beimplemented by executing one or more operations as illustrated in FIG. 8. The third image may combine the merits of the first image and thesecond image. The third image may present reduced artifacts and improvedCT number uniformity. For example, the third image and the second imagemay present a better CT number uniformity than the first image. Asanother example, the third image and the first image may have fewerartifacts than the second image. More descriptions of the third imagemay be found elsewhere in the disclosure (e.g., FIG. 8 and thedescription thereof).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theprocess 700 may include an operation for pre-processing the projectiondata before 720. Exemplary pre-processing operation may include, forexample, projection data normalization, projection data smoothing,projection data suppressing, projection data encoding (or decoding),preliminary denoising, etc.

FIG. 8 is a flowchart illustrating an exemplary process 800 forgenerating an image according to some embodiments of the presentdisclosure. The process 800 may be executed by the imaging system 100.For example, the process 800 may be stored in the storage device 140and/or the storage 220 in the form of instructions (e.g., anapplication), and invoked and/or executed by the processing device 120(e.g., the processor 210 illustrated in FIG. 2 , or one or more modulesin the processing device 120 illustrated in FIG. 4 ). The operations ofthe illustrated process presented below are intended to be illustrative.In some embodiments, the process 800 may be accomplished with one ormore additional operations not described, and/or without one or more ofthe operations discussed. Additionally, the order in which theoperations of the process 800 as illustrated in FIG. 8 and describedbelow is not intended to be limiting. In some embodiments, thegeneration of the third image illustrated in the operation 740 of FIG. 7may be implemented by performing one or more operations of the process800.

In 810, the image generation module 430 (e.g., the image subtractionunit 610) may generate a difference image based on a first image and asecond image by, e.g., subtraction. In some embodiments, the imagegeneration module 430 may obtain the first image and/or the second imageof an object by executing one or more operations as illustrated in FIG.7 or FIG. 9 . For example, the first image and/or the second image maybe obtained in connection with the operation 720 and/or the operation730 as illustrated in FIG. 7 . In some embodiments, the image generationmodule 430 may obtain the first image and the second image from thestorage device 140, a storage module of the processing device 120, or anexternal storage device.

In some embodiments, the image generation module 430 may generate thedifference image by subtracting one image from the other. For example,the image generation module 430 may generate the difference image bysubtracting the first image from the second image. As another example,the image generation module 430 may generate the difference image bysubtracting the second image from the first image. The image subtractionmay refer to a subtraction operation between data (e.g., gray values) ofcorresponding pixels/voxels in the first image and the second image. Asused herein, a pixel/voxel of an image may be referred to ascorresponding to a pixel/voxel of another image when each of thepixels/voxels of the two images corresponds to a same part of an objectrepresented in the images. For example, the image generation module 430may subtract the gray value of a voxel in the corner region of the firstimage by the gray value of the corresponding voxel in the correspondingcorner region of second image. As described elsewhere in the presentdisclosure, the first image may include better high frequency componentsthan the second image, the corner region of the first image may includebetter low frequency components than the corresponding corner region ofthe second image, and the center region of the second image may includebetter low frequency components than the corresponding center region ofthe first image. To obtain a third image that combines the merits of thefirst image and the second image described above, the image generationmodule 430 may generate the difference image by subtracting the firstimage from the second image. By performing the subtraction operation anda low-pass filtering operation (will be described in detail in operation840), the image generation module 430 may obtain the third imageincluding better low frequency components in the center region comparedto that of the first image and better high frequency components comparedto that of the second image.

In 820, the image generation module 430 (e.g., the image masking unit620) may generate a fourth image by performing a masking operation onthe difference image. The difference image of the object may includefour corner regions (e.g., the four corner regions 1004-1, 1004-2,1004-3, and 1004-4 illustrated in FIG. 10 ) and a center region (e.g.,the center region 1006 illustrated in FIG. 10 ). In some embodiments,the boundary between the four corner regions and the center region maybe default settings of the imaging system 100, or may be adjustableunder different situations. In some embodiments, the boundary may form ashape in a plane of the difference image. For example, as illustrated inFIG. 10 , the boundary may form two cone surfaces in a plane of theimage region 1002.

To perform the masking operation on the difference image, the imagegeneration module 430 may provide a mask to the difference image. Themask may include a matrix (e.g., a two-dimensional matrix, athree-dimensional matrix), or a binary image in which the gray value ofa pixel (or voxel) may be “0” or “1.” Merely by way of example, theelements of the matrix corresponding to the corner regions of thedifference image have the value of zero, and the elements of the matrixcorresponding to the center region of the difference image have thevalue of one. By applying the mask on the difference image (e.g., makingthe mask multiply by the difference image), the image generation module430 may reset the values of the pixels/voxels (e.g., gray values) in thefour corner regions of the difference image to a default value (e.g.,“0” for gray value), and the values of the pixels/voxels (e.g., grayvalues) at the center region of the difference image may remainunchanged.

For illustration purposes, the image in FIG. 13 -A is provided as anexemplary fourth image according to some embodiments of the presentdisclosure. As illustrated in FIG. 13 -A, the gray values of the pixelsin the four corner regions indicated by the arrows A1, A2, A3, and A4are set to be “0.”

In 830, the image generation module 430 (e.g., the data extrapolationunit 630) may generate a fifth image by performing a data extrapolationoperation on the fourth image. A data extrapolation operation may be aprocess in which the value of a specific data point is estimated basedon the values of data points in the vicinity of the specific data pointin space. By performing the data extrapolation operation on the fourthimage, the image generation module 430 may assign the values of apixel/voxel (e.g., gray value) in a corner region of the fourth imagebased on the values of pixels/voxels (e.g., gray values) in the centerregion of the fourth image. In some embodiments, the image generationmodule 430 may set the values of pixels/voxels in a corner regionaccording to the values of pixels/voxels that are in the center regionand closest to the corner region (e.g., at a boundary between the cornerregion and the center region). For example, the gray values of pixels ona vertical line segment in the corner region may be equal to the grayvalue of the pixel which is connected to the vertical line segment andat a boundary between the corner region and the center region.

In 840, the image generation module 430 (e.g., the low-pass filteringunit 640) may generate a sixth image by performing a low-pass filteringoperation on the fifth image. The image generation module 430 mayperform the low-pass filtering operation on the fifth image using alow-pass filter, such as, a Gaussian low-pass filter, a Butterworthlow-pass filter, a Chebyshev low-pass filter, a 3D box filter (e.g., a3×3×3 box filter), or the like, or any combination thereof. Byperforming the low-pass filtering operation to generate the sixth image,the image generation module 430 may reduce or remove the high frequencycomponents of the fifth image and retain the low frequency components ofthe fifth image. In some embodiments, the image generation module 430may perform one or more rounds of low-pass filtering on the fifth image.The parameters of the low-pass filtering in different rounds may be thesame or different. For example, the cutoff frequencies of the low-passfiltering in different rounds may be different.

For illustration purposes, the image in FIG. 13 -C is provided as anexemplary sixth image according to some embodiments of the presentdisclosure. Compared to the regions indicated by the arrows A1′, A2′,A3′, and A4′ in the fifth image illustrated in FIG. 13 -B, highfrequency components in the regions indicated by the arrows A1″, A2″,A3″, and A4″ are reduced or removed.

In 850, the image generation module 430 (e.g., the image combinationunit 650) may combine the sixth image and the first image to generate athird image. The image combination may refer to a combination of data(e.g., gray value) of corresponding pixels/voxels in the sixth image andthe first image. During the image combination, the image generationmodule 430 may apply one or more of various operations including, forexample, an addition operation, a subtraction operation, amultiplication operation, a division operation, or any combinationthereof. In some embodiments, the processing device 120 may combine thesixth image and the first image via a non-linear combination. Thenon-linear combination may be performed according to detected edgeinformation and structure information in the sixth image and the firstimage. Structure information in an image may refer to high frequencycomponents of the image. For example, the structure information in thesixth image or the first image of an object may correspond to askeleton, an organ, etc., of the object.

The third image may show the same part of the object as the first imageand the second image. For example, the third image may also have cornerregions showing the corner part(s) of the object and a center regionshowing the center part of the object.

As described in connection with the operation 740 in the process 700,the third image may combine the merits of the first image and the secondimage. For example, the third image and the first image may have fewerartifacts than the second image. As illustrated in FIG. 14 -A throughFIG. 14 -C, the regions indicated by the arrows A″ and B″ in FIG. 14 -B(e.g., the third image) and the regions indicated by the arrows A and Bin FIG. 14 -A (e.g., the first image) present fewer artifacts than theregions indicated by the arrows A′ and B′ in FIG. 14 -C (e.g., thesecond image).

Additionally, the third image and the second image may present a betterCT number uniformity than the first image. As illustrated in FIG. 15 -A(e.g., the first image), the average brightness of the left regionindicated by the arrow E is significantly lower than that of the rightregion indicated by the arrow F. As illustrated in FIGS. 15 -B and 15-C,the average brightness of the left region indicated by the arrow E″ (orE′) is relatively close to that of the right region indicated by thearrow F″ (or F′).

Further, the third image and the first image may present better highfrequency components than the second image. As illustrated in FIG. 15 -A(e.g., the first image), FIG. 15 -B (e.g., the third image), and FIG. 15-C (e.g., the second image), regions indicated by the arrow A, A′, or A″have several high frequency components. Compared to the region indicatedby the arrow A′, artifacts are reduced in the region indicated by thearrow A″ and/or in the region indicated by the arrow A. Further, thecorner region(s) in the third image and/or in the first image maypresent better low frequency components than the corner region(s) of thesecond image. As illustrated in FIGS. 15 -A to 15-C, regions indicatedby the arrow C, C′, or C″ have several low frequency components.Compared to the region indicated by the arrow C′, artifacts are reducedin the region indicated by the arrow C″ and/or in the region indicatedby the arrow C. Similarly, the center region in the third image and/orin the second image may present better low frequency components than thecenter region in the first image. For example, low frequency componentsof the center region in the third image and/or in the second image mayinclude few artifacts than low frequency components of the correspondingcenter region in the first image.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theprocess 800 may include a storing operation for storing intermediateresults (e.g., the difference image, the fourth image, the fifth image,etc.) during the generation of the third image.

FIG. 9 is a flowchart illustrating an exemplary process 900 forgenerating an image according to some embodiments of the presentdisclosure. The process 900 may be executed by the imaging system 100.For example, the process 900 may be stored in the storage device 140and/or the storage 220 in the form of instructions (e.g., anapplication), and invoked and/or executed by the processing device 120(e.g., the processor 210 illustrated in FIG. 2 , or one or more modulesin the processing device 120 illustrated in FIG. 4 ). The operations ofthe illustrated process presented below are intended to be illustrative.In some embodiments, the process 900 may be accomplished with one ormore additional operations not described, and/or without one or more ofthe operations discussed. Additionally, the order in which theoperations of the process 900 as illustrated in FIG. 9 and describedbelow is not intended to be limiting. In some embodiments, thegeneration of the first image as illustrated in the operation 720 ofFIG. 7 and/or the generation of the second image as illustrated in theoperation 730 of FIG. 7 may be implemented by performing one or moreoperations of the process 900.

In 910, the image reconstruction module 420 may select a first voxelfrom a plurality of first voxels corresponding to a first image. In someembodiments, the first image may include a plurality of first voxels.

In 920, the image reconstruction module 420 (e.g., the weighting unit510) may apply, according to a first weighting function, a weightingfactor to first projection data of the first voxel corresponding to eachof a plurality of projection angles to obtain weighted projection dataof the first voxel.

In some embodiments, the weighting factors applied to the firstprojection data of the first voxel according to different projectionangles may be different. As illustrated in FIG. 11 , the radiationsource may scan an object 1102 along a circular trajectory 1103. Whenthe radiation source is located at position 1101 (corresponding to theprojection angle of 13), a radiation ray 1104 (e.g., an X-ray) may beemitted from the radiation source to a detector 1105 (e.g., a flatdetector). The radiation ray 1104 may pass through a voxel 1106 (e.g.,the first voxel) of the object 1102 and form a projection point 1107 onthe detector 1105. The detector 1105 may be associated with a localcoordinate system defined by the origin O′ and the u-axis and thev-axis. The direction of the v-axis may be parallel to the direction ofthe Z-axis of the XYZ coordinate system as illustrated. The origin ofthe local coordinate system O′ may be in the plane of the origin O. Whenthe radiation source moves along the circular trajectory, the voxel 1106may be radiated by the radiation source from a plurality of projectionangles, thus forming different projection points on the detector 1105.In some embodiments, the weighting factor applied to the firstprojection data corresponding to a specific projection angle may beassociated with the position of the projection point on the detector1105 at the specific projection angle. For example, a first weightingfactor associated with a first projection point located at the centerpoint of the detector 1105 may be greater than a second weighting factorassociated with a second projection point located at an edge point ofthe detector 1105.

For better understanding the weighting factors applied to the firstprojection data, an exemplary illustration is provided in the following,which is not intended to be limiting.

The weighting function (e.g., the first weighting function describedherein) may be an aperture weighting function as illustrated in FIG. 12. In some embodiments, the aperture weighting function may be expressedas Equation (2):

$\begin{matrix}{{A(z)} = \left\{ {\begin{matrix}{{\sin^{2}\left( {\frac{\pi}{2}\frac{E - z}{E - Q}} \right)}\ ,} & {Q < z < E} \\{1.,} & {{- Q} \leq z \leq Q} \\{{\sin^{2}\left( {\frac{\pi}{2}\frac{E + z}{E - Q}} \right)}\ ,} & {{- E} < z < {- Q}}\end{matrix},} \right.} & {{Equation}(2)}\end{matrix}$where A(z) represents the aperture weighting function, z represents theposition of the projection point at the detector 1105 along the v-axisor the Z-axis illustrated in FIG. 11 , denoted as a value in the Z-axisin FIG. 12 , Q represents a first parameter of the aperture weightingfunction A(z), and E represents a second parameter of the apertureweighting function A(z). It shall be noted that, when the projectionpoint is located within a center range of the detector 1105 (i.e.,−Q≤z≤Q), the value of the aperture weighting function A(z) is aconstant. When the projection point is located beyond the center rangeof the detector 1105, the value of the aperture weighting function A(z)decreases with the increase of the distance between the projection pointand the center point 0 illustrated in FIG. 12 . The first parameter Qmay be associated with the dimension of the center range of thedetector, and the second parameter E may be associated with thedimension of the descending trend of the aperture weighting functionA(z) beyond the center range. In some embodiments, the first parameter Qand the second parameter E may be in proportional to the width W of thedetector 1105. For example, the value of Q may be 0.6*W, and the valueof E may be 1.1*W. For brevity, the values of (Q,E) may be expressed as(0.6*W, 1.1*W). As another example, the value of Q may be 1.0*W, thevalue of E may be 1.1*W, and the values of (Q,E) may be expressed as(1.0*W, 1.1*W).

The assignment of different values of the first parameter Q and thesecond parameter E may generate different weighting functions, such as,the first weighting function and the second function illustrated inconnection with the operations 720 and 730 in the process 700. Thevalues of the first parameter Q and the second parameter E may affectthe qualities of the first image and the second image. In someembodiments, the first weighting function and the second weightingfunction may be assigned the same value of the second parameter EAdditionally, the first weighting function may be assigned a smallervalue of the first parameter Q than the second weighting function.Accordingly, as described elsewhere in the disclosure, the first imagemay include fewer artifacts and better high frequency components thanthe second image. Further, a corner region of the first image mayinclude better low frequency components than the corresponding cornerregion of the second image. Moreover, the second image may present abetter CT number uniformity than the first image. The center region ofthe second image may include better low frequency components than thecorresponding center region of the first image.

For example, in FIGS. 14 -A and 14-C, the values of (Q,E) assigned inreconstructing the image in FIG. 14 -A (e.g. the first image) are (0.0,1.1), and the values of (Q,E) assigned in reconstructing the image inFIG. 14 -C (e.g., the second image) are (1.0, 1.1). As shown in FIG. 14-A, compared to the regions indicated by the arrows A′ and B′ in FIG. 14-C, artifacts are reduced in the regions indicated by the arrows A and Bin FIG. 14 -A.

As another example, in FIGS. 15 -A and 15-C, the values of (Q,E)assigned in reconstructing the image in FIG. 15 -A (e.g. the firstimage) are (0.0, 1.1), and the values of (Q,E) assigned inreconstructing the image in FIG. 15 -C (e.g., the second image) are(1.0, 1.1). As shown in FIG. 15 -A, the average brightness of the leftregion indicated by the arrow E is significantly lower than the rightregion indicated by the arrow F. As shown in FIG. 15 -C, the averagebrightness of the left region indicated by the arrow E′ is relativelyclose to the right region indicated by the arrow F′. Therefore, theimage in FIG. 15 -C presents a better CT number uniformity than theimage in FIG. 15 -A.

In some embodiments, the weighting factor applied to the firstprojection data corresponding to a specific projection angle may bedetermined based on one or more values of the aperture weightingfunction A(z). For example, the weighting factor for the firstprojection data of the first voxel may be a normalized value of a firstvalue and a second value of the aperture weighting function A(z). Insome embodiments, the normalization may provide a good CT numberuniformity for the first image. In some embodiments, the normalizationmay be a linear normalization in which the weight may be describedaccording to Equation (3):w=w1/(w1+w2),  Equation (3)where w represents the weighting factor applied to the first projectiondata of the first voxel corresponding to a projection angle, w1represents the first value of the aperture weighting function A(z), andw2 represents the second value of the aperture weighting function A(z).

Alternatively, the normalization may be described according to Equation(4):w=w1/(w1+a ² w2),  Equation (4)where a represents a coefficient which may de determined by theweighting unit 510 or set manually by a user.

In some embodiments, the first value w1 of the aperture weightingfunction A(z) may be associated with a first projection point on adetector where radiation from the radiation source at the projectionangle strikes and the second value w2 of the aperture weighting functionA(z) may be associated with a second projection point on the detectorwhere radiation from the radiation source at an opposite projectionangle strikes. The difference between the opposite projection angle andthe projection angle may be 180° or −180°. For example, as illustratedin FIG. 11 , when the radiation source is located at the position 1101,the projection angle is β. The radiation ray 1104 passing through thevoxel 1106 forms the first projection point (i.e., the projection point1107) on the detector 1105. The opposite projection angle of theprojection angle β is 180°+β or β−180°. The position of the radiationsource at the opposite projection angle (e.g., the position 1109 asshown in FIG. 11 ) and the position 1101 are symmetrical with respect tothe origin O illustrated in FIG. 11 . When the radiation source islocated at the position corresponding to the opposite projection angle,a radiation ray passing through the voxel 1106 forms the secondprojection point (not shown) on the detector 1105. As described above,the first projection point and the second projection point maycorrespond to the first value w1 of the first weighting function and thesecond value w2 of the first weighting function, respectively.

In 930, the image reconstruction module 420 (e.g., the back projectionunit 520) may back-project the weighted projection data of the firstvoxel to obtain back-projected data of the first voxel. Theback-projected data of the first voxel may refer to the voxel value ofthe first voxel. In some embodiments, the image reconstruction module420 may determine a BP value of the first voxel for each of theplurality of projection angles. The image reconstruction module 420 mayadd up all BP values of the first voxel, each corresponding to one ofthe plurality of projection angles, to obtain the back-projected data ofthe first voxel.

In 940, the image reconstruction module 420 may determine whether allthe first voxels corresponding to the first image have been selected. Ifnot all the first voxels are selected, the process 900 may return backto the operation 910 to select a new first voxel from the rest firstvoxels, and repeat the operations 920 and 930. If all the first voxelshave been selected, the process 900 may proceed to the operation 950 toobtain the first image based on the back-projected data of the firstvoxel.

In some embodiments, the image reconstruction module 420 may beimplemented on a parallel hardware architecture. The parallel hardwarearchitecture may include a plurality of processing threads (e.g.,processing thread “A” and processing thread “B”) to execute operationsconcurrently. The processing thread “A” may perform at least one of theoperations illustrated in FIG. 9 to generate the first image, andconcurrently the processing thread “B” may perform at least one ofcorresponding operations illustrated in FIG. 9 to generate the secondimage.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, beforethe operation 930, the process 900 may include a convolution operationperformed on the weighted projection data of the first voxel (e.g.,calculating the convolution of the weighted projection data of the firstvoxel and a filter function (e.g., an S-L filter function)) to obtainenhanced edge sharpness in the first image. As another example, theimage reconstruction module 420 may adjust the values of (Q,E) in thefirst weighting function and/or the second weighting function accordingto the object to be reconstructed. For example, for the head and abreast of a patient, the processing device 120 may apply differentvalues of (Q,E) in the first weighting function and/or the secondweighting function.

EXAMPLES

The following examples are provided for illustration purposes, and arenot intended to limit the scope of the present disclosure.

FIG. 13 -A is an exemplary image according to some embodiments of thepresent disclosure. The image is a CT image generated based on theprojection data of an object (e.g., a breast). The arrows A1, A2, A3,and A4 indicate portions of the image corresponding to corner parts ofthe object. The arrow B indicates a portion of the image correspondingto a center part of the object. The gray values of the pixels in theportions of the image indicated by the arrows A1, A2, A3, and A4 are“0.”

FIG. 13 -B is an exemplary image generated based on a data extrapolationoperation performed on FIG. 13 -A according to some embodiments of thepresent disclosure. The arrows A1′, A2′, A3′, and A4′ indicate portionsof the image corresponding to corner parts of the object. The arrow B′indicates a portion of the image corresponding to a center part of theobject.

FIG. 13 -C is an exemplary image generated based on a low-filteringoperation performed on FIG. 13 -B according to some embodiments of thepresent disclosure. The arrows A″, A2″, A3″, and A4″ indicate portionsof the image corresponding to corner parts of the object. The arrow B″indicates a portion of the image corresponding to a center part of theobject.

FIGS. 14 -A to 14-C illustrate three exemplary images according to someembodiments of the present disclosure. The three images are sagittal CTimages related to the chest reconstructed according to an apertureweighting function with different values of (Q,E). The values of (Q,E)assigned in reconstructing the image in FIG. 14 -A are (0.0, 1.1), andthe values of (Q,E) assigned in reconstructing the image in FIG. 14 -Care (1.0, 1.1). The image in FIG. 14 -B was generated based on the imagein FIG. 14 -A and the image in FIG. 14 -C by performing the process 800as illustrated in FIG. 8 .

FIGS. 15 -A to 15-C illustrate three exemplary images according to someembodiments of the present disclosure. The three images are axial CTimages related to the chest reconstructed according to an apertureweighting function with different values of (Q,E). The values of (Q,E)assigned in reconstructing the image in FIG. 15 -A are (0.0, 1.1), andthe values of (Q,E) assigned in reconstructing the image in FIG. 15 -Care (1.0, 1.1). The image in FIG. 15 -B was generated based on the imagein FIG. 15 -A and the image in FIG. 15 -C by performing the process 800illustrated in FIG. 8 .

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by the present disclosure andare within the spirit and scope of the exemplary embodiments of thepresent disclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer-readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electromagnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2103, Perl,COBOL 2102, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as asoftware-only solution, for example, an installation on an existingserver or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. A system, comprising: at least one storage device includinga set of instructions; and at least one processor in communication withthe at least one storage device, wherein when executing theinstructions, the at least one processor is configured to cause thesystem to perform operations including: obtaining projection data of anobject, wherein the projection data is generated by a scanner viascanning the object which is located in a scanning region of thescanner; generating, based on a first weighting function, a first imagecorresponding to the object by back-projecting the projection data,wherein the first image includes a first region corresponding to atleast a part of the object; generating, based on a second weightingfunction, a second image corresponding to the object by back-projectingthe projection data, wherein the second image includes a second regioncorresponding to the at least part of the object, and the second regionof the second image presenting a better CT number uniformity than thefirst region of the first image; and generating a third image of theobject based on the first image and the second image.
 2. The system ofclaim 1, wherein the first image has fewer artifacts than the secondimage.
 3. The system of claim 1, wherein the first image includes betterhigh frequency components than the second image.
 4. The system of claim1, wherein the scanner further includes: a radiation source configuredto scan the object along a circular trajectory covering an angle rangeof 360° to produce the projection data.
 5. The system of claim 4,wherein the at least part of the object is radiated by the radiationsource at an angle range less than 360°, and the first region of thefirst image includes better low frequency components than the secondregion of the second image.
 6. The system of claim 1, wherein thescanning region of the scanner includes a first scanning region and asecond scanning region, and the first weighting function includes afirst weighting factor and a second weighting factor, the firstweighting factor relating to a first angle range of the first scanningregion, the second weighting factor relating to a second angle range ofthe second scanning region, wherein the first weighting factor isassigned to a first part of the projection data corresponding to thefirst scanning region; the second weighting factor is assigned to asecond part of the projection data corresponding to the second scanningregion; and the first weighting factor is different from the secondweighting factor.
 7. The system of claim 6, wherein the first scanningregion is a corner part of the scanning region and the first part of theprojection data is generated from a first set of radiation beams of thescanner that pass through a first corner part of the object; and thesecond scanning region is a center part of the scanning region and thesecond part of the projection data is generated from a second set ofradiation beams of the scanner that pass through a center part of theobject.
 8. The system of claim 1, wherein the scanning region of thescanner includes a third scanning region and a fourth scanning region,and the second weighting function includes a third weighting factor anda fourth weighting factor, the third weighting factor relating to athird angle range of the third scanning region, the fourth weightingfactor relating to a fourth angle range of the fourth scanning region,wherein the third weighting factor is assigned to a third part of theprojection data corresponding to the third scanning region in which asecond corner part of the object is located; the fourth weighting factoris assigned to a fourth part of the projection data corresponding to thefourth scanning region in which a center part of the object is located;and the third weighting factor is different from the fourth weightingfactor.
 9. The system of claim 8, wherein the second region of thesecond image corresponds to the second corner part of the object. 10.The system of claim 1, wherein to generate a third image, the at leastone processor is configured to cause the system to perform theoperations including: generating a difference image of the first imageand the second image by subtraction; and determining the third imagebased on the difference image and the first image.
 11. A method forimage generation implemented on at least one machine each of whichincludes at least one processor and at least one storage device, themethod comprising: obtaining projection data of an object, wherein theprojection data is generated by a scanner via scanning the object whichis located in a scanning region of the scanner; generating, based on afirst weighting function, a first image corresponding to the object byback-projecting the projection data, wherein the first image includes afirst region corresponding to at least a part of the object; generating,based on a second weighting function, a second image corresponding tothe object by back-projecting the projection data, wherein the secondimage includes a second region corresponding to the at least part of theobject, and the second region of the second image presenting a better CTnumber uniformity than the first region of the first image; andgenerating a third image of the object based on the first image and thesecond image.
 12. The method of claim 11, wherein the first image hasfewer artifacts than the second image.
 13. The method of claim 11,wherein the first image includes better high frequency components thanthe second image.
 14. The method of claim 11, wherein the scannerfurther includes: a radiation source configured to scan the object alonga circular trajectory covering an angle range of 360° to produce theprojection data.
 15. The method of claim 14, wherein the at least partof the object is radiated by the radiation source at an angle range lessthan 360°, and the first region of the first image includes better lowfrequency components than the second region of the second image.
 16. Themethod of claim 14, wherein the scanning region of the scanner includesa first scanning region and a second scanning region, and the firstweighting function includes a first weighting factor and a secondweighting factor, the first weighting factor relating to a first anglerange of the first scanning region, the second weighting factor relatingto a second angle range of the second scanning region, wherein the firstweighting factor is assigned to a first part of the projection datacorresponding to the first scanning region; the second weighting factoris assigned to a second part of the projection data corresponding to thesecond scanning region; and the first weighting factor is different fromthe second weighting factor.
 17. The method of claim 16, wherein thefirst scanning region is a corner part of the scanning region and thefirst part of the projection data is generated from a first set ofradiation beams of the scanner that pass through a first corner part ofthe object; and the second scanning region is a center part of thescanning region and the second part of the projection data is generatedfrom a second set of radiation beams of the scanner that pass through acenter part of the object.
 18. The method of claim 11, wherein thescanning region of the scanner includes a third scanning region and afourth scanning region, and the second weighting function includes athird weighting factor and a fourth weighting factor, the thirdweighting factor relating to a third angle range of the third scanningregion, the fourth weighting factor relating to a fourth angle range ofthe fourth scanning region, wherein the third weighting factor isassigned to a third part of the projection data corresponding to thethird scanning region in which a second corner part of the object islocated; the fourth weighting factor is assigned to a fourth part of theprojection data corresponding to the fourth scanning region in which acenter part of the object is located; and the third weighting factor isdifferent from the fourth weighting factor.
 19. The method of claim 18,wherein the second region of the second image corresponds to the secondcorner part of the object.
 20. A non-transitory computer readable mediumembodying a computer program product, the computer program productcomprising instructions configured to cause a computing device toperform operations including: obtaining projection data of an object,wherein the projection data is generated by a scanner via scanning theobject which is located in a scanning region of the scanner; generating,based on a first weighting function, a first image corresponding to theobject by back-projecting the projection data, wherein the first imageincludes a first region corresponding to at least a part of the object;generating, based on a second weighting function, a second imagecorresponding to the object by back-projecting the projection data,wherein the second image includes a second region corresponding to theat least part of the object, and the second region of the second imagepresenting a better CT number uniformity than the first region of thefirst image; and generating a third image of the object based on thefirst image and the second image.